ZeyuBian
R/methods.R
changed.
R/lspath_strong.R
changed.
R/lspath_strong_weights.R
changed.
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@@ -50,6 +50,9 @@
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| 50 | 50 | #' predict(fit, s = 0.45) # predicted response for a single lambda value |
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| 51 | 51 | #' predict(fit, s = c(2.15, 0.32, 0.40), type="nonzero") # nonzero coefficients |
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| 52 | 52 | #' @seealso \code{\link{predict.cv.sail}} |
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| 53 | + | #' @note When the coef method is called, the alpha values, which represent the |
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| 54 | + | #' interaction term are returned. This alpha is the product of beta_e,gamma_j |
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| 55 | + | #' and theta_j |
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| 53 | 56 | #' @rdname predict.sail |
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| 54 | 57 | #' @export |
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| 55 | 58 | predict.sail <- function(object, newx, newe, s = NULL, |
@@ -60,7 +60,7 @@
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| 60 | 60 | # this is used for the predict function |
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| 61 | 61 | design <- expansion$design |
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| 62 | 62 | ||
| 63 | - | nulldev <- as.numeric(crossprod(y - mean(y))) |
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| 63 | + | nulldev <- as.numeric(crossprod(sqrt(weights)*(y - mean(y)))) |
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| 64 | 64 | ||
| 65 | 65 | # Initialize ------------------------------------------------------------- |
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| 66 | 66 | # the initial values here dont matter, since at Lambda_max everything is 0 |
@@ -430,7 +430,7 @@
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| 430 | 430 | if (abs(environ[lambdaIndex]) > 0) "E" |
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| 431 | 431 | ) |
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| 432 | 432 | ||
| 433 | - | deviance <- crossprod(R.star) |
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| 433 | + | deviance <- crossprod(sqrt(weights)*R.star) |
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| 434 | 434 | devRatio <- 1 - deviance / nulldev |
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| 435 | 435 | dfbeta <- sum(abs(betaMat[, lambdaIndex]) > 0) / ifelse(expand, ncols, 1) |
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| 436 | 436 | dfalpha <- sum(abs(alphaMat[, lambdaIndex]) > 0) / ifelse(expand, ncols, 1) |
@@ -59,7 +59,7 @@
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| 59 | 59 | # this is used for the predict function |
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| 60 | 60 | design <- expansion$design |
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| 61 | 61 | ||
| 62 | - | nulldev <- as.numeric(crossprod(y - mean(y))) |
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| 62 | + | nulldev <- as.numeric(crossprod(sqrt(weights)*(y-mean(y)))) |
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| 63 | 63 | ||
| 64 | 64 | # Initialize ------------------------------------------------------------- |
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| 65 | 65 | # the initial values here dont matter, since at Lambda_max everything is 0 |
@@ -253,9 +253,6 @@
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| 253 | 253 | # |
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| 254 | 254 | # R.star <- R.star + Delta |
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| 255 | 255 | ||
| 256 | - | ||
| 257 | - | ||
| 258 | - | ||
| 259 | 256 | # converged_theta <- FALSE |
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| 260 | 257 | # k <- 1 |
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| 261 | 258 | # while (!converged_theta && k < maxit){ |
@@ -269,7 +266,7 @@
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| 269 | 266 | gglasso = coef(gglasso::gglasso( |
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| 270 | 267 | x = x_tilde_2[[j]], |
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| 271 | 268 | y = R, |
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| 272 | - | weights=weights, |
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| 269 | + | weight=diag(weights), |
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| 273 | 270 | # eps = 1e-12, |
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| 274 | 271 | maxit = 100000, |
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| 275 | 272 | group = if (expand) rep(1, ncols) else rep(1, ncols[j]), |
@@ -315,8 +312,9 @@
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| 315 | 312 | gglasso = coef(gglasso::gglasso( |
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| 316 | 313 | x = x_tilde_2[[j]], |
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| 317 | 314 | y = R, |
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| 315 | + | ||
| 318 | 316 | # eps = 1e-12, |
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| 319 | - | weights=weights, |
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| 317 | + | weight=diag(weights), |
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| 320 | 318 | group = if (expand) rep(1, ncols) else rep(1, ncols[j]), |
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| 321 | 319 | pf = wj[j], |
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| 322 | 320 | lambda = LAMBDA * (1 - alpha), |
@@ -448,7 +446,7 @@
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| 448 | 446 | if (abs(environ[lambdaIndex]) > 0) "E" |
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| 449 | 447 | ) |
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| 450 | 448 | ||
| 451 | - | deviance <- crossprod(R.star) |
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| 449 | + | deviance <- crossprod(sqrt(weights)*R.star) |
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| 452 | 450 | devRatio <- 1 - deviance / nulldev |
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| 453 | 451 | dfbeta <- sum(abs(betaMat[, lambdaIndex]) > 0) / ifelse(expand, ncols, 1) |
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| 454 | 452 | dfalpha <- sum(abs(alphaMat[, lambdaIndex]) > 0) / ifelse(expand, ncols, 1) |
| Files | Coverage |
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| R | 53.53% |
| Project Totals (11 files) | 53.53% |
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